1. Field of the Invention
The present invention relates to methods and systems for the digital processing of radiological images, and it more specifically relates to an automated method and system for the re-screening and detection of abnormalities, such as lung nodules in radiological chest images using multi-resolution processing, digital image processing and artificial neural networks.
2. Background Art
Lung cancer is the leading type of cancer in both men and women worldwide. Early detection and treatment of localized lung cancer at a potentially curable stage can significantly increase the patients' survival rate. Studies have shown that approximately 68% of retrospectively detected lung cancers were detected by one reader and approximately 82% were detected with an additional reader as a "second-reader". A long-term lung cancer screening program conducted at the Mayo Clinic found that 90% of peripheral lung cancers were visible in small sizes in retrospect, in earlier radiographs.
Among the common detection techniques, such as chest X-ray, analysis of the types of cells in sputum specimens, and fiber optic examination of bronchial passages, chest radiography remains the most effective and widely used method. Although skilled pulmonary radiologist can achieve a high degree of accuracy in diagnosis, problems remain in the detection of the lung nodules in chest radiography due to errors that cannot be corrected by current methods of training even with a high level of clinical skill and experience.
An analysis of the human error in diagnosis of lung cancer revealed that about 30% of the misses were due to search errors, about 25% of the misses were due to recognition errors, and about 45% of the misses were due to decision-making errors. Reference is made to Kundel, H. L., et al., "Visual Scanning, Pattern Recognition and Decision-Making in Pulmonary Nodule Detection", Investigative Radiology, May-June 1978, pages 175-181, and Kundel, H. L., et al., "Visual Dwell Indicated Locations of False-Positive and False-Negative Decisions", Investigative Radiology, June 1989, Vol. 24, pages 472-478, which are incorporated herein by reference. The analysis suggested that the miss rates for the detection of small lung nodules could be reduced by about 55% with a computerized method. According to the article by Stitik, F. P., "Radiographic Screening in the Early Detection of Lung Cancer", Radiologic Clinics of North America, Vol. XVI, No. 3, December 1978, pages 347-366, which is incorporated herein by reference, many of the missed lesions would be classified as T1M0 lesions, the stage of non-small cell lung cancer that Mountain, C. F. "Value of the New TNM Staging System for Lung Cancer", 5.sup.th World Conference in Lung Cancer Chest, 1989 Vol. 96/1, pages 47-49, which is incorporated herein by reference, indicates has the best prognosis (42%, 5 year survival). It is this stage of lung cancer, with lesions less than 1.5 cm in diameter, and located outside the hilum region that need to be detected usually by a radiologist.
Commputerized techniques, such as computer aided diagnosis (CAD), have been introduced to assist in the diagnosis of lung nodules during the stage of non-small cell lung cancer. The CAD technique required the computer system to function as a second physician to double check all the films that a primary or first physician has examined. Reduction of false positive detection is the primary objective of the CAD technique in order to improve detection accuracy.
Several CAD techniques using digital image processing and artificial neural networks have been described in numerous publications, examples of which are the following, which are incorporated herein by reference:
U.S. Pat. No. 4,907,156 to Doi et al. describes a method for detecting and displaying abnormal anatomic regions existing in a digital X-ray image. A single projection digital X-ray image is processed to obtain signal-enhanced image data with a maximum signal-to-noise ratio (SNR) and is also processed to obtain signal-suppressed image data with a suppressed SNR. Then, difference image data are formed by subtraction of the signal-suppressed image data from the signal-enhanced image data to remove low-frequency structured anatomic background, which is basically the same in both the signal-suppressed and signal-enhanced image data. Once the structured background is removed, feature extraction, is formed. For the detection of lung nodules, pixel thresholding is performed, followed by circularity and/or size testing of contiguous pixels surviving thresholding. Threshold levels are varied, and the effect of varying the threshold on circularity and size is used to detect nodules. For the detection of mammographic microcalcifications, pixel thresholding and contiguous pixel area thresholding are performed. Clusters of suspected abnormalities are then detected. However, the algorithm described in the Doi et al. patent seems to reduce false positive rates at the expense of missing several true nodules. This prior art is limited in detection of nodules with size larger than its pre-selected size--1.5 cm. This prior art will also reduce the sensitivity by selecting fixed CDF thresholds (e.g., 97%, 94%, 91%, etc.) since some true nodules will be eliminated during this thresholding process. The algorithm described in the Doi et al. patent utilizes a single classifier (a decision tree classifier) which possesses limited performance compared to multiple classification schemes presented below. The use of a decision tree classifier performs classification in eliminating true positives in sequential way, hence it is easy to eliminate potential nodules in the first decision node even if the rest of the decision criteria are satisfied. Another important drawback in this prior art is that physician has to examine every film with both true and false positives identified by the CAD system, such that the time spent on the diagnosis increases dramatically.
U.S. Pat. No. 5,463,548 to Asada et al. describes a system for computer-aided differential diagnosis of diseases, and in particular, computer-aided differential diagnosis using neural networks. A first design of the neural network distinguishes between a plurality of interstitial lung diseases on the basis of inputted clinical parameters and radiographic information. A second design distinguishes between malignant and benign mammographic cases based upon similar inputted clinical and radiographic information. The neural networks were first trained using a hypothetical database made up of hypothetical cases for each of the interstitial lung diseases and for malignant and benign cases. The performance of the neural network was evaluated using receiver operating characteristics (ROC) analysis. The decision performance of the neural network was compared to experienced radiologists and achieved a high performance comparable to that of the experienced radiologists. The neural network according to the invention can be made up of a single network or a plurality of successive or parallel networks. The neural network according to the invention can also be interfaced to a computer which provides computerized automated lung texture analysis to supply radiographic input data in an automated manner. However Asada's method seems limited to the detection of lung diseases but not lung cancer, which present different symptoms.
Y. S. P. Chiou, Y. M. F. Lure, and P. A. Ligomenides, "Neural Network Image Analysis and Classification in Hybrid Lung Nodule Detection (HLND) System", Neural Networks for Processing III Proceedings of the 1993 IEEE-SP Workshop, pp. 517-526. The chiou et al. article described a Hybrid Lung Nodule Detection (HLND) system based on artificial neural network architectures, which is developed for improving diagnostic accuracy and speed of lung cancerous pulmonary radiology. The configuration of the HLND system includes the following processing phases: (1) pre-processing to enhance the figure-background contrast; (2) quick selection of nodule suspects based upon the most pertinent feature of nodules; and (3) complete feature space determination and neural classification of nodules. The Chiou et al. article seems to be based on U.S. Pat. No. 4,907,156 to Doi et al., but adds a neural network approach. The Chiou et al. system includes similar shortcoming as the Doi et al. system described in U.S. Pat. No. 4,907,156.
S. C. Lo, J. S. Lin, M. T. Freedman, and S. K. Mun, "Computer-Assisted Diagnosis of Lung Nodule Detection Using Artificial Convolution Neural Network", Proceeding of SPIE Medical Imaging VI, Vol. 1898, 1993. This article describes nodule detection methods using a convolutional neural network consisting of a two-dimensional connection trained with a back propagation learning algorithm, in addition to thresholding and circularity calculation, morphological operation, and a 2-D sphere profile matching technique. The use of a very complicated neural network architecture, which was originally developed for optical character recognition in binary images, the lengthy training time, and the lack of focus on the reduction of false positives, render the published nodule detection methods impractical. This prior art also possesses similar drawback as the Doi et al. system described in U.S. Pat. No. 4,907,156.
S. C. Lo, S. L. Lou, S. Lin, M. T. Freedman, and S. K. Mun, "Artificial convolution neural network techniques for lung nodule detection", IEEE Trans. Med. Imag. Vol 14, pp 711-718, 1995. This article describes nodule detection methods using a convolution neural network consisting of a two-dimensional connection trained with a back propagation learning algorithm, in addition to thresholding and circularity calculation, morphological operation, and a 2-D sphere profile matching technique. This prior art also possesses similar drawback as the Doi et al. system and that described in Lo, et al., 1993.
J-S Lin, P. Ligomenides, S-C B. Lo, M. T. Freedman, S. K. Mun, "A Hybrid Neural-Digital Computer Aided Diagnosis System for Lung Nodule Detection on Digitized Chest Radiographs", Proc. 1994 IEEE Seventh Symp. on Computer Based Medical Systems, pp. 207-212, describes a system for the detection and classification of cancerous lung nodules utilizing image processing and neural network. However, the system described in this article suffers from similar shortcomings as the system described in the Lo et al. article.
M. L. Giger, "Computerized Scheme for the Detection of Pulmonary Nodules", Image Processing VI, IEEE Engineering in Medicine & Biology Society, 11.sup.th Annual International Conference (1989), describes a computerized method to detect locations of lung nodules in digital chest images. The method is based on a difference-image approach and various feature-extraction techniques including a growth test, a slope test, and a profile test. The aim of the detection scheme is to direct the radiologist's attention to locations in an image that may contain a pulmonary nodule, in order to improve the detection performance of the radiologist. However, the system described in the article suffers from similar shortcomings as the system described in U.S. Pat. No. 4,907,156 to Doi et al.